Genome sequence of Phormia regina Meigen (Diptera: Calliphoridae): implications for medical, veterinary and forensic research

BACKGROUND: Blow flies (Diptera: Calliphoridae) are important medical, veterinary and forensic insects encompassing 8 % of the species diversity observed in the calyptrate insects. Few genomic resources exist to understand the diversity and evolution of this group. RESULTS: We present the hybrid (short and long reads) draft assemblies of the male and female genomes of the common North American blow fly, Phormia regina (Diptera: Calliphoridae). The 550 and 534 Mb draft assemblies contained 8312 and 9490 predicted genes in the female and male genomes, respectively; including > 93 % conserved eukaryotic genes. Putative X and Y chromosomes (21 and 14 Mb, respectively) were assembled and annotated. The P. regina genomes appear to contain few mobile genetic elements, an almost complete absence of SINEs, and most of the repetitive landscape consists of simple repetitive sequences. Candidate gene approaches were undertaken to annotate insecticide resistance, sex-determining, chemoreceptors, and antimicrobial peptides. CONCLUSIONS: This work yielded a robust, reliable reference calliphorid genome from a species located in the middle of a calliphorid phylogeny. By adding an additional blow fly genome, the ability to tease apart what might be true of general calliphorids vs. what is specific of two distinct lineages now exists. This resource will provide a strong foundation for future studies into the evolution, population structure, behavior, and physiology of all blow flies

Coordinating Coronavirus Research: The COVID-19 Infectious Disease Ontology

Rapidly, accurately and easily interpreting generated data is of fundamental concern. Ontologies – structured controlled vocabularies – support interoperability and prevent the development of data silos which undermine interoperability. The Open Biological and Biomedical Ontologies (OBO) Foundry serves to ensure ontologies remain interoperable through adherence by its members to core ontology design principles. For example, the Infectious Disease Ontology (IDO) Core includes terminological content common to investigations of all infectious diseases. Ontologies covering more specific infectious diseases in turn extend from IDOCore, such as the Coronavirus Infectious Disease Ontology (CIDO). The growing list of virus-specific IDO extensions has motivated construction of a reference ontology covering content common to viral infectious disease investigations: the Virus Infectious Disease Ontology (VIDO). Additionally the present pandemic has motivated construction of a more specific extension of CIDO covering terminological contents specific to the pandemic: the COVID-19 Infectious Disease Ontology (IDO-COVID-19). We report here the development of VIDO and IDO-COVID-19. More specifically we examine newly minted terms for each ontology, showcase reuse of terms from existing OBO ontologies, motivate choicepoints for ontological decisions based on research from relevant life sciences, apply ontology terms to explicate viral pathogenesis, and discuss the annotating power of virus ontologies for use in machine-learning projects

Implementation of machine learning for the evaluation of mastitis and antimicrobial resistance in dairy cows

Bovine mastitis is one of the biggest concerns in the dairy industry, where it affects sustainable milk production, farm economy and animal health. Most of the mastitis pathogens are bacterial in origin and accurate diagnosis of them enables understanding the epidemiology, outbreak prevention and rapid cure of the disease. This thesis aimed to provide a diagnostic solution that couples Matrix-Assisted Laser Desorption/Ionization-Time of Flight (MALDI-TOF) mass spectroscopy coupled with machine learning (ML), for detecting bovine mastitis pathogens at the subspecies level based on their phenotypic characters. In Chapter 3, MALDI-TOF coupled with ML was performed to discriminate bovine mastitis-causing Streptococcus uberis based on transmission routes; contagious and environmental. S. uberis isolates collected from dairy farms across England and Wales were compared within and between farms. The findings of this chapter suggested that the proposed methodology has the potential of successful classification at the farm level. In Chapter 4, MALDI-TOF coupled with ML was performed to show proteomic differences between bovine mastitis-causing Escherichia coli isolates with different clinical outcomes (clinical and subclinical) and disease phenotype (persistent and non-persistent). The findings of this chapter showed that phenotypic differences can be detected by the proposed methodology even for genotypically identical isolates. In Chapter 5, MALDI-TOF coupled with ML was performed to differentiate benzylpenicillin signatures of bovine mastitis-causing Staphylococcus aureus isolates. The findings of this chapter presented that the proposed methodology enables fast, affordable and effective diag-nostic solution for targeting resistant bacteria in dairy cows. Having shown this methodology successfully worked for differentiating benzylpenicillin resistant and susceptible S. aureus isolates in Chapter 5, the same technique was applied to other mastitis agents Enterococcus faecalis and Enterococcus faecium and for profiling other antimicrobials besides benzylpenicillin in Chapter 6. The findings of this chapter demonstrated that MALDI-TOF coupled with ML allows monitoring the disease epidemiology and provides suggestions for adjusting farm management strategies. Taken together, this thesis highlights that MALDI-TOF coupled with ML is capable of dis-criminating bovine mastitis pathogens at subspecies level based on transmission route, clinical outcome and antimicrobial resistance profile, which could be used as a diagnostic tool for bo-vine mastitis at dairy farms

A comparison of the factors which influence infection control in paediatric wards in England and Thailand.

Acquiring an infection during a hospital stay is a hazard for patients throughout the world. Over 1.4 million people worldwide are suffering from infections acquired in hospital. Five to ten per cent of patients admitted to modern hospitals in developed countries acquire one or more infections, whereas patients in developing countries have a higher risk, around two to twenty times this figure. Paediatric patients, especially neonates and infants, have an additional risk of infection because of their compromised immune system. The purpose of this study was to explore the factors which contribute to the spread of infection among children in paediatric wards in a developed and a developing country: England and Thailand. Method: An ethnographic approach was utilised to identify practices which promote or prevent the spread of infection in each country. Purposive sampling was employed to recruit ten nurses in England and ten nurses in Thailand. Ethical approval was obtained from De Montfort University (DMU), National Research Ethics Service and the ethical approval committee in Thailand. Nonparticipant observations and semi-structured interviews were the main methods of obtaining data in clinical settings. Data from the observations and interviews were transcribed and coded using thematic content analysis. Results: Hospitals in Thailand and England faced the same problems regarding attitudes, values and beliefs which contribute to infection control difficulties in children, particularly poor hand hygiene. Good attitudes and beliefs will promote good practice. Moreover, education and training can raise perceptions and promote good practice. However, in terms of different cultures and circumstances, the key factors explaining different implementations between the two countries are resources, lifestyle, and religion. Conclusion: Even within the same hospital, different backgrounds including education, cultures, policies and support result in different factors which impact on paediatric patients. Individuality and personal responsibility for infection control practice are the most significant factors influencing compliance with best practice

Implementation of machine learning for the evaluation of mastitis and antimicrobial resistance in dairy cows

Bovine mastitis is one of the biggest concerns in the dairy industry, where it affects sustainable milk production, farm economy and animal health. Most of the mastitis pathogens are bacterial in origin and accurate diagnosis of them enables understanding the epidemiology, outbreak prevention and rapid cure of the disease. This thesis aimed to provide a diagnostic solution that couples Matrix-Assisted Laser Desorption/Ionization-Time of Flight (MALDI-TOF) mass spectroscopy coupled with machine learning (ML), for detecting bovine mastitis pathogens at the subspecies level based on their phenotypic characters. In Chapter 3, MALDI-TOF coupled with ML was performed to discriminate bovine mastitis-causing Streptococcus uberis based on transmission routes; contagious and environmental. S. uberis isolates collected from dairy farms across England and Wales were compared within and between farms. The findings of this chapter suggested that the proposed methodology has the potential of successful classification at the farm level. In Chapter 4, MALDI-TOF coupled with ML was performed to show proteomic differences between bovine mastitis-causing Escherichia coli isolates with different clinical outcomes (clinical and subclinical) and disease phenotype (persistent and non-persistent). The findings of this chapter showed that phenotypic differences can be detected by the proposed methodology even for genotypically identical isolates. In Chapter 5, MALDI-TOF coupled with ML was performed to differentiate benzylpenicillin signatures of bovine mastitis-causing Staphylococcus aureus isolates. The findings of this chapter presented that the proposed methodology enables fast, affordable and effective diag-nostic solution for targeting resistant bacteria in dairy cows. Having shown this methodology successfully worked for differentiating benzylpenicillin resistant and susceptible S. aureus isolates in Chapter 5, the same technique was applied to other mastitis agents Enterococcus faecalis and Enterococcus faecium and for profiling other antimicrobials besides benzylpenicillin in Chapter 6. The findings of this chapter demonstrated that MALDI-TOF coupled with ML allows monitoring the disease epidemiology and provides suggestions for adjusting farm management strategies. Taken together, this thesis highlights that MALDI-TOF coupled with ML is capable of dis-criminating bovine mastitis pathogens at subspecies level based on transmission route, clinical outcome and antimicrobial resistance profile, which could be used as a diagnostic tool for bo-vine mastitis at dairy farms

Developing a knowledge management framework for facilities management services for the control of exogenous healthcare associated infections (HCAI) in NHS hospitals

The occurrence and adverse complications arising from healthcare associated infections (HCAIs) have been well documented as one of the leading causes of morbidity and mortality by successive governments and healthcare professional bodies in the United Kingdom (UK). The significance of these challenges has led to a plethora of published regulatory guidance and continuous surveillance tools that focus on good practice. These good practice guidance documents are aimed at reducing the prevalence of both endogenous (internal) and exogenous (external) healthcare associated infections in NHS hospitals. However, there is an acknowledgment that a huge gap still exists between the implementation of knowledge accumulated over the years from the use of these good practice guidance documents and the monitoring tools adopted for benchmarking compliance with good practice in the prevention of HCAIs. With the increasing evidence of the contribution of the healthcare environment to the prevalence of HCAIs, expert opinion has affirmed that the scope for the prevention of healthcare-associated infections no longer rests only within the remit of medicines, but includes other key service providers to the healthcare sector, among which the facilities management discipline is paramount. This has led to a research need for a better understanding of the subtleties of knowledge management processes in healthcare facilities management practice. The aim of this research is to examine the issues of the knowledge management process, i.e. the creation, storing, sharing and usage of knowledge in hospital facilities management cleaning services for the control of exogenous HCAIs. This investigation was carried out within the context of hospital knowledge infrastructure capabilities, which consist of the prevailing culture, structure and technological capabilities. The research is premised on an interpretivist research philosophy and utilises a sequential explanatory mixed methodology approach. This approach consists of the synthesis and review of literature pertinent to the research subject domain, a questionnaire survey and face-to-face interviews. Quantitative data was obtained from a questionnaire survey of 81 NHS hospital facilities managers in England, and was subjected to rigorous statistical analysis. Qualitative data was obtained from face-to-face interviews with 10 NHS facilities managers and subjected to thematic analysis using NVivo software. Findings across the three data collection instruments were contextualised and subjected to further rigorous statistical analysis using the Relative Importance Index (RII), also known as the “weighted models”, to ascertain the empirical importance of the variables. The findings obtained were used to develop a good practice knowledge management framework. Findings from the research showed that efficient management of compliance with good practice guidance protocols in the control of exogenous HCIAs is could be achieve through the provision of cleaning services using directly employed in-house staff. This enables a high level of collaboration between the clinician members of an infection control team and a hospital facilities manager in the control of exogenous HCAIs. The majority of the NHS hospitals surveyed use their bespoke good practice guidance documents in the delivery of hospital cleaning services. These bespoke guidance documents are a combination of key performance indicators (KPIs) drawn from prevailing statutory core guidance documents. There is evidence of a lack of appreciation and understanding of the relevance of interfacing knowledge management processes to the hospital knowledge infrastructure capabilities in the delivery of facilities management cleaning services for efficient control of exogenous HCAIs. A conceptual knowledge management framework representing the fundamental empirical interface of the knowledge management process elements within the hospital knowledge infrastructure capabilities was developed to assist in the control of exogenous HCAIs through facilities management cleaning service delivery practices in NHS hospitals. This framework was validated by facilities managers across NHS hospitals in England to ascertain its feasibility from both the analytical (scientific) and pragmatic (operational) perspectives

Open Data

Open data is freely usable, reusable, or redistributable by anybody, provided there are safeguards in place that protect the data’s integrity and transparency. This book describes how data retrieved from public open data repositories can improve the learning qualities of digital networking, particularly performance and reliability. Chapters address such topics as knowledge extraction, Open Government Data (OGD), public dashboards, intrusion detection, and artificial intelligence in healthcare